{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "'电影名称':['California Man','He/s Not Really into Dudes','Beautiful Woman','Kevin longblabe','Robo Slayer 3000','Amped II','?'],\n",
    "'打斗镜头':[3,2,1,101,99,98,18],\n",
    "'接吻镜头':[104,100,81,10,5,2,90],\n",
    "'电影类型':['爱情片','爱情片','爱情片','动作片','动作片','动作片','未知'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>电影类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Man</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He/s Not Really into Dudes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beautiful Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin longblabe</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robo Slayer 3000</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amped II</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>?</td>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         电影名称  打斗镜头  接吻镜头 电影类型\n",
       "0              California Man     3   104  爱情片\n",
       "1  He/s Not Really into Dudes     2   100  爱情片\n",
       "2             Beautiful Woman     1    81  爱情片\n",
       "3             Kevin longblabe   101    10  动作片\n",
       "4            Robo Slayer 3000    99     5  动作片\n",
       "5                    Amped II    98     2  动作片\n",
       "6                           ?    18    90   未知"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>电影类型</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>?</td>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>未知</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He/s Not Really into Dudes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>爱情片</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beautiful Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>爱情片</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Man</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>爱情片</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin longblabe</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>动作片</td>\n",
       "      <td>13289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robo Slayer 3000</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>动作片</td>\n",
       "      <td>13786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amped II</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>动作片</td>\n",
       "      <td>14144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         电影名称  打斗镜头  接吻镜头 电影类型   dist\n",
       "6                           ?    18    90   未知      0\n",
       "1  He/s Not Really into Dudes     2   100  爱情片    356\n",
       "2             Beautiful Woman     1    81  爱情片    370\n",
       "0              California Man     3   104  爱情片    421\n",
       "3             Kevin longblabe   101    10  动作片  13289\n",
       "4            Robo Slayer 3000    99     5  动作片  13786\n",
       "5                    Amped II    98     2  动作片  14144"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = (df.iloc[:,1:3] - [18,90])**2\n",
    "df2 = df1.sum(axis=1).sort_values()\n",
    "df['dist']=df2\n",
    "df.sort_values(axis=0, by='dist', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dist\n",
       "356      1\n",
       "370      1\n",
       "421      1\n",
       "13289    1\n",
       "13786    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(axis=0, by='dist', ascending=True)\n",
    "k=5\n",
    "df.iloc[:k,:].value_counts('dist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3\n",
       "1      2\n",
       "2      1\n",
       "3    101\n",
       "4     99\n",
       "5     98\n",
       "6     18\n",
       "Name: 打斗镜头, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kiss')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df.iloc[:,1],y=df.iloc[:,2],c=['green','green','green','red','red','red','red'],marker=\"x\")\n",
    "plt.xlabel('fight')\n",
    "plt.ylabel('kiss')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit ('3.10.6')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6 (main, Nov 24 2022, 14:20:32) [GCC 9.4.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d44d76ef8cbbc4331cecfe2e59228ac31ebb71026289858a116838be7168b60b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
